Methods and systems for detecting and recognizing objects in a controlled wide area

ABSTRACT

Methods and systems are described herein for high-speed observation and recognition of objects or persons within a controlled area using 3D image data and tomography, stereo-photogrammetry, range finding and/or structured illumination. A wide area security system may comprise multiple pairs of 3D sensors surrounding a controlled area. Using at least two pairs of 3D sensors and a first 3D data collection technique such as tomography, a zone of interest within the controlled area is detected. Using at least one pair of 3D sensors and a second 3D data collection technique such as structured illumination, more 3D image data related to an object of interest is collected. The 3D image data may be compared to data representing known objects and used to identify an object of interest. Methods for fast processing of 3D data and recognition of objects of interest are also described.

RELATED APPLICATIONS

[0001] This application claims the benefit of priority of provisionalapplication No. 60/383,216 filed May 22, 2002, which is incorporatedherein by reference.

FIELD OF THE INVENTION

[0002] The present invention relates to methods and systems forthree-dimensional (“3D”) identification and recognition of objects orpersons. More particularly, the invention relates to methods and systemsfor providing security systems that identify objects or persons using 3Dimage data.

BACKGROUND OF THE INVENTION

[0003] In today's world, security and access control systems forprotecting secure or restricted areas, such as airports, governmentdefense facilities, or corporate sites containing confidentialproprietary information, are in high demand. To be effective, securitysystems for these types of areas must first of all be accurate, that is,a security system must accurately determine both objects or persons whoare authorized to enter as well as those objects or persons who must notbe granted access. Effective security systems for high volume areas,such as airports, must be fast and easy-to-use. Systems that further addto delay at airports or require a traveler to carry something wouldfurther inconvenience the traveler and therefore be unacceptable.

[0004] For these reasons, many conventional security systems usebiometric data, such as fingerprints, retinal eye patterns, or handgeometry, to identify a person. The captured biometric data is comparedto a database of biometric data representing known persons. If theacquired data matches the profile data of an individual from thedatabase, the person may be granted access to a secure facility oridentified as someone who should not be granted access. Theseconventional security systems, however, typically require cooperation ofa target, such as a person, and therefore are not designed for“non-cooperative” scenarios, wherein biometric data often must beacquired passively, that is, without requiring any special action,effort or participation of the target to be identified.

[0005] Biometric identification systems which do not require a target'scooperation are enjoying great popularity and demand among authoritiesand companies where security is or is becoming important, such asairports, banks, workplaces and other secure or restricted places. Forinstance, systems employing biometric facial recognition, unlikesecurity systems that require target cooperation (e.g., fingerprintrecognition, iris recognition, etc.), require no human cooperation,awareness, or contact, work passively at a distance in real timeenvironment, and can be easily integrated into existing securitysystems. Consequently, biometric facial recognition systems are suitablefor non-intrusive security checks at high-risk public sites (airports,borders, etc.), and for passive identification and monitoring of knowncriminals and terrorists.

[0006] Conventional systems and methods for biometric facial recognitiontypically use two-dimensional (“2D”) images of a person's face, similarto images received from video or photo cameras. Although 2D image datais easy to collect, it is not uniquely distinctive and the quality ofthe acquired data is dependent on a variety of conditions, such asambient light conditions, view angle, etc. Consequently, the reliabilityof 2D biometric facial recognition systems lags behind many conventionalsecurity systems that use biometric data, such as fingerprints, retinaleye patterns, or hand geometry, to identify a person.

[0007] Some conventional systems, such as those only capable ofcapturing 2D image data, experience difficulty in isolating a targetimage, such as a person's face from other objects, and consequentlyexperience difficulty in identifying the target image. Such systems alsoexperience accuracy problems because the quality of the acquired data isnegatively affected by shadows on, the angle of, or movement by theperson or object to be identified. In addition, these systems experiencedifficulties in making assumptions about the target image based on thetarget's shape because they do not have 3D image data and therefore donot have distance measurements. Furthermore, many of these systems donot reject corrupted image data, further increasing their probability oferror.

[0008] Other conventional security systems, though capable of capturing3D image data for target identification, require cooperation of a targetfor capturing image data and therefore have the same disadvantages asconventional security systems that require target cooperation, e.g.,fingerprint recognition, iris recognition, etc. For instance, inconventional 3D image recognition systems, a person may have toknowingly stay in or move through a designated area armed with cameras,such as a portal or doorway, to facilitate image data capture. Somesecurity systems using 3D data do not require cooperation of the target;however, they experience difficulties isolating a target image from asea of unwanted images, such as trying to isolate one person from acrowd of people in an airport. In addition, speed, accuracy, andportability have been recurrent and difficult to goals to achievesimultaneously for devices that scan, measure or otherwise collectgeometric data about 3D objects for identification and recognition.

[0009] Still other methods and systems that collect 3D image data arelimited by the way the 3D image data is collected. For example, singledot optical scanning systems determines the location of the 3D objectbased on the angle of reflection of a single point of reflected light.Such systems can digitize only a single point at a time and thereforeare relatively slow in collecting a full set of points to describe anobject and are further limited by the precision of the movement andpositioning of the laser beam. Scan line systems that employ atwo-dimensional (“2D”) imager, such as a charge coupled device (“CCD”)camera, for signal detection projects a light plane (i.e., a laserstripe) instead of just one dot and reads the reflection of multiplepoints depicting the contour of an object at a location that is at adistance from the CCD camera and from which the position can betriangulated. Such systems typically use a bulky, high-precisionmechanical system for moving the scanner and are further limited becausethe laser stripe stays at a fixed angle relative to the camera and thesystem makes its calculations based on the cylindrical coordinates ofits rotating platform. The mathematical simplicity in such a projectionsystem complicates the hardware portion of these devices as theytypically depend on the rotational platform mentioned. Also, thesimplified geometry does not generally allow for extremely refinedreproduction of topologically nontrivial objects, such as objects withholes in them (e.g., a tea pot with a handle). Full realization oftriangulation scanning with a non-restrictive geometry has not beenachieved in the available devices.

[0010] Deficiencies in still other known methods and systems forthree-dimensional identification and recognition occur in the matchingprocess. Methods and systems that collect too much data on the acquiredperson or object or attempt to increase accuracy by increasing thenumber of attributes compared may suffer performance problems, that is,recognition of the person or object may take too long for practicalapplication. This deficiency must be balanced against collecting toolittle information or comparing too few features thereby resulting in asystem that is so inaccurate as to not be useful.

SUMMARY OF THE INVENTION

[0011] Methods and systems for identifying an object of interest in acontrolled area surrounded by at least two pairs of 3D sensors aredescribed herein. In certain embodiments of the present invention, first3D image data representing the controlled area is collected using atleast two pairs of 3D sensors surrounding the controlled area. A zone ofinterest within the controlled area is detected based on the first 3Dimage data. Second image data representing the detected zone of interestis collected utilizing at least one pair of 3D sensors. An object ofinterest in the controlled area is identified based on the second 3Dimage data. In certain embodiments, an object of interest within thedetected zone of interest is detected based on the second 3D image dataand third 3D image data representing the detected object of interest iscollected utilizing at least one 3D sensor. The third 3D image data maybe compared to data representing known objects and used to identify anobject of interest.

DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings, which are incorporated in, andconstitute a part of the specification, illustrate implementations ofthe invention and, together with the detailed description, serve toexplain the principles of the invention. In the drawings,

[0013]FIG. 1 shows a functional layout of a system consistent with thepresent invention;

[0014]FIG. 2 is a flow chart illustrating an exemplary method of 3Dimage recognition;

[0015]FIG. 3 is a flow chart illustrating an exemplary method ofrejecting 3D image data;

[0016]FIG. 4 illustrates a functional layout of one embodiment of a 3Dsensor consistent with the present invention;

[0017]FIG. 5 is a schematic diagram of an illumination source unitconsistent with the present invention;

[0018]FIG. 6 is a schematic diagram of a multi-channel detection unitwith photoregistrar and processor consistent with the present invention;

[0019]FIG. 7a is a schematic diagram of an object image in structuredillumination mode coded in binary code;

[0020]FIG. 7b is a schematic diagram of an object image in structuredillumination mode coded in binary code;

[0021]FIG. 8 shows a functional layout of one exemplary embodiment of asystem consistent with the present invention;

[0022]FIG. 9 shows a functional layout of one exemplary embodiment of asystem consistent with the present invention;

[0023]FIG. 10 is a flow chart illustrating an exemplary method of 3Dimage recognition;

[0024]FIG. 11a is a schematic diagram of image projections of twoobjects constructed using computed tomography;

[0025]FIG. 11b is a schematic diagram of reverse image projections oftwo objects; and

[0026]FIG. 12 is a schematic diagram of two 3D sensors usingtriangulation.

DETAILED DESCRIPTION

[0027] Consistent with the present invention, a 3D recognition systemacquires 3D image data relating to an object for computer-aidedprocessing of that 3D image data, and identifies the object. Theprinciples of the present invention may be applied in a variety ofdisciplines, such as the fields of security and high-speed surveillance,where the principles of the present invention may be used to capture,detect, and recognize an image of a subject or groups of subjects orother target of interest within an area. An exemplary system may includeone or more 3D sensing units or 3D sensors.

[0028] For instance, the principles of the present invention areparticularly useful in detecting subjects or objects of interest wherethe subject or object of interest is situated within a designated area.One such example is where a person stands in front of a 3D sensor thatis capable of acquiring 3D image data and using that image data for 3Dfacial recognition.

[0029] Additionally, the principles of the present invention may be usedto detect subjects or objects of interest where the subject or object ofinterest is within or moves through an area, such as a portal. One suchexample is detecting a terrorist that moves through an access portal,such as a security entrance. Another example is verifying that a subjecthas a known access level (e.g., a frequent flyer status, VIP status,employment status, etc.) and, based on the subject's access level,granting the subject access to a place of business (e.g., a bank,theater, workplace, shop, etc.), i.e., confirming that the subject's 3Dimage data matches data stored in a database of subjects with knownaccess levels.

[0030] The principles of the present invention are also useful fordetecting subjects or objects of interest where the subject or object ofinterest may be part of a group of similar objects or subjects in acontrolled area. One such example is detecting a terrorist in a crowdedairport. Another example is tracking a known person, such as anemployee, within an area, such as an airport, bank or other building.

[0031] Consistent with the present invention, the principles ofstructured illumination, tomography, stereo-photogrammetry, rangefinding and triangulation combined with computerized image recognitionmay be used in a 3D recognition and surveillance security system.

[0032] A Single Sensing Unit 3D Recognition System

[0033] Consistent with the present invention, FIG. 1 shows thefunctional layout of one exemplary embodiment that provides recognitionof an object that is situated within a designated area, e.g., in frontof a 3D sensing unit. System 100 comprises at least one 3D sensing unit,i.e., 3D sensor 120. 3D sensor 120 may include one or more illuminatingunits and one or more detecting units. 3D sensor 120 may be configuredsuch that it is capable of gathering 3D image data of an object ofinterest that is situated in front of it, such as subject 110, andidentifying the object based on the 3D image data.

[0034] 3D sensor 120 may be operatively connected to processor 130 and,directly or indirectly to, memory 140 via bus 150. In one embodiment,processor 130 taken together with memory 140 form a computer 160.Computer 160 may be operatively connected to an input device 170, suchas an identity card reader which performs verification to controlaccess. In addition, computer 160 may be operatively connected to anoutput device 180, such as a door lock or alarm generator, and to anetwork and/or server 190.

[0035] In other embodiments, some or all of the 3D sensor and/orprocessor 130, and/or memory 140 may be implemented using or distributedacross one or more computers. A computer may include conventionalcomponents such as a processor, memory (e.g. RAM), a bus which couplesprocessor and memory, a mass storage device (e.g. a magnetic hard diskor an optical storage disk) coupled to processor and memory through anI/O controller and a network interface, such as a conventional modem. Itwill be appreciated that the present invention may be implemented insoftware which is stored as executable instructions on a computerreadable medium in a computer system, such as a mass storage device, ormemory. Rules, such as the rules for constructing feature vectors,described herein, and other data may be stored in, for example, memoryor mass storage on a computer system.

[0036] Consistent with the present invention, any of the processorsdescribed herein may be microprocessors such as the Pentium® familymicroprocessors manufactured by Intel Corporation. However, any othersuitable microprocessor, micro-, mini-, or mainframe computer, may beused. In addition, memory may include a random access memory (RAM), aread-only memory (ROM), a video memory, or mass storage. Memories maycontain a program, such as an operating system, an applicationprogramming interface (API), and other instructions for performing themethods consistent with the invention. Mass storage may include bothfixed and removable media (e.g., magnetic, optical, or magnetic opticalstorage systems or other available mass storage technology).

[0037] Consistent with the present invention, a 3D recognition system,such as the system depicted in FIG. 1, may comprise one or more 3Dsensors that acquire 3D image data representing a 3D image of an objectfor subsequent computer-aided recognition of the object. An exemplary 3Drecognition method for processing the 3D image data and identifying theobject consistent with the present invention is shown in FIG. 2. Asdepicted in FIG. 2, a 3D recognition system first acquires 3D image datarepresenting a 3D image of an object (Stage 210). The 3D image data maybe acquired by any suitable means for acquiring robust 3D image data.

[0038] Methods and systems consistent with the present invention use,for example, structured illumination and/or stereo-photogrammetry.

[0039] Structured illumination (also called structured lighting) is afront-lighting technique used to extract surface features from 3Dobjects and to reconstruct the geometry of the object's surface. Instructured lighting, a light pattern (line, grid or other pattern) isprojected onto an object at a known angle using a light source orprojector. The light source may be any type of light emitting deviceincluding, for example, laser, light-emitting diode (“LED”), inert gaslamp, incandescent lamp or other working in visible, ultraviolet orinfrared range. The light pattern intersects with an object, and isreflected according to the contours of the object. Detectors detect thedeflected light and the observed distortions in the line can betranslated into height and depth variations. Structured lighting issometimes described as “active triangulation.”

[0040] Stereo-photogrammetry is a technique used to obtain 3Dmeasurements of an object through the process of recording and measuringa pair of images taken by a pair of detectors. In particular,stereo-photogrammetry is used to determine the third coordinate of thespatial location of a point namely a Z-coordinate called distance ordepth. The mathematical principles of stereo-photogrammetry are wellknown to those skilled in the art.

[0041] In one exemplary method, 3D image data may be received by seriesacquisition and 3D images may be processed using principles of computedtomography and principles described in commonly-assigned published PCTApplication Number WO 02/75244, which claims priority to Russian PatentNo. 200110752 filed Mar. 19, 2001, both of which are hereby expresslyincorporated by reference. In that invention, a method is described forcontactless control of linear sizes of 3D objects. The method includesthe repeated illumination of an object's surface by a beam of opticalradiation under different foreshortenings and registration of flatimages of the illuminated surface parts at each illuminationforeshortening. To restore the object's surface topology, the flatimages are reverse projected. Each reverse projection is rotated at anangle corresponding to the respective illumination foreshortening andthe rotated reverse projections are summarized.

[0042] In that invention, illumination of the object's surface iscarried out using structured illumination probing and spatial modulationof an optical radiation beam. The registration of flat images of theilluminated parts of a surface is carried out on a direction distinctfrom normal. The system also determines the height of a controllableobject's surface based on the degree of image distortion of the probingilluminating structure, that is, by measuring the change in position ofthe illuminated structure in the registered image.

[0043] In another exemplary method, 3D images are formed usingstructured illumination using N different sources of optical radiation,each of a different radiation spectral range, and N photodetectors, eachof which has an area of spectral sensitivity conterminous to a radiationspectral range of one of the N radiation sources. The illumination ofeach of the N sources is structured by a corresponding spatial lightmodulator (SLM), wherein an SLM may produce an aperiodic line structureof strips which differs from other SLMs. In this method, the structuredillumination from the N sources is projected by an optical system, e.g.,an afocal optical system, on the object's surface, distorted by asurface relief of the object and collected by the N photodetectors. Thecollected images are converted by corresponding electronic units todigital signals and preprocessed. The images are summed and thecoordinates of the object's surface are computed using the formula:${Z = \frac{\Delta \quad Y}{{tg}\quad \alpha}},$

[0044] wherein Z is the height of a surface profile at a point withcoordinates X, Y, intersected by any strip of a line structure; ΔY isthe value of a strip's curvature at that point; and α is the anglebetween the direction of radiation of a source of an optical image andthe objective optical axis.

[0045] In one exemplary method, the number “N” is determined by theformula N=log 2(L), where L is the spatial resolution of thereconstructed 3D surface, calculated as the number of surface profilesto be measured. At least one such exemplary embodiment is described inco-pending, co-assigned Russian patent application number 2001133293,filed Dec. 11, 2001, hereby expressly incorporated herein by reference.This method may be used to determine a 3D image of, for example, anobject or a controlled area. Difficulty arises, however, in providingrecognition of a large field of view, for instance recognition of acontrolled area, while at the same time focusing on and providingrecognition of one or more objects within that controlled area.

[0046] Returning to FIG. 2, the system determines the data thatrepresents the 3D image of the object's surface based on the acquired 3Dimage data (Stage 215). The system processes the 3D image datarepresenting the 3D image of the object's surface, for example, byfiltering out noise (Stage 220). The system checks the quality of theprocessed 3D image data representing the 3D image of the object, forexample, by using a rejection algorithm, such as the rejection algorithmdepicted in FIG. 3, described below (Stage 225).

[0047] The system constructs a feature vector based on the processeddata representing the 3D image of the object (Stage 230). The featurevector may include data representing measurements of anthropologicalmetrics, such as, for example, the distance between a person's eyes,length of a nose, or size of a forehead. The system constructs a filtervector based on the feature vector (Stage 235). For instance, the systemmay use a set of predetermined criteria to make basic assumptions aboutthe object based on the feature vector. For example, the system may useforensic criteria to predict the gender, ethnic group, or age of aperson. The system may then construct the filter vector based on theseassumptions. These assumptions may take the form of rules, such as, forexample, if the width of the forehead is under nine centimeters, theobject is a person under the age of fourteen.

[0048] Using the filter vector, the system “indexes” a database, i.e.,it determines a subset of the database (Stage 240). The database, forexample, may include data representing biometric features of objects orpersons known at the time of processing. The system then compares thedata representing the 3D image of the object to data within the databasesubset (Stage 245). Comparing the 3D image data to a subset of thedatabase as opposed to the entire database consumes less processingpower and in some cases, less processing time. Upon finding a matchbetween the data representing the 3D image of the object and a data inthe database, the object may be identified (Stage 250).

[0049] In at least one embodiment, the 3D image data is compared with,for example, a database of biometric data of 3D objects-of-interest. Incertain embodiments, the database may include biometric datarepresenting known or suspected terrorists. Biometric data may include,for example, measurements between eyes, nose length, size of forehead,gender, etc. This biometric data in the database may be based ondescriptions and/or actual images of suspected or known criminals thatwere gathered in the course of law enforcement. The database may residein the memory of the sensing unit, the memories associated with one ormore of the 3D sensors, a memory attached to a 3D sensor via a bus, orwithin another such memory.

[0050] In at least one embodiment, the system performs a rejectionalgorithm to check the quality of the data representing the 3D imageprior to using that image data for comparison to a database. FIG. 3 is aflow chart illustrating an exemplary rejection algorithm consistent withthe principles of the present invention. As depicted in FIG. 3, datarepresenting a 3D image of the object is obtained (Stage 310). If the 3Dimage data is determined to be “good” (Stage 320), the 3D image data maybe retained (Stage 330). 3D image data may be “good,” for example, if itpasses a threshold of predetermined criteria. If 3D image data is notgood, a different frame of image data representing another 3D image isobtained (Stage 310). If the 3D image data is retained, it is determinedif enough data has been retained for recognition (Stage 340). There maybe enough data for recognition, for example, if the amount of 3D imagedata meets a predetermined criteria, such as exceeding a thresholdminimum amount of data. The system may then use that 3D image data, forexample, to identify an object. If, however, there is not enough datafor recognition, a second 3D image data representing a 3D image of theobject is obtained (Stage 310).

[0051] Exemplary 3D Sensors

[0052] Consistent with the present invention, a 3D recognition system,such as the system depicted in FIG. 1, may comprise one or more 3Dsensors that acquire 3D image data. FIG. 4 shows the functional layoutof one embodiment of a 3D sensor 400 consistent with the presentinvention. In the exemplary embodiment shown in FIG. 4, 3D sensor 400comprises illuminating unit 401, control unit 402, photoregistrar 404,detecting unit 405, control unit 406, and optionally signal processor420 and memory 425. A 3D sensor that includes signal processor 420and/or memory 425 may otherwise be referred to as a “smart 3D sensor.”

[0053] Illuminating unit 401 may be any suitable light-emitting devicesuch as, for example, laser, light-emitting diode (“LED”), inert gaslamp, incandescent lamp or other working in visible, ultraviolet orinfrared range. In certain embodiments, the illumination is provided bya flash or strobe light, which has a very short duration andconsequently may be preferable when illuminating moving objects. Incertain embodiments of the present invention, illuminating unit 401 maybe capable of illuminating an object evenly and, in the otherembodiments, it may project a patterned light (such as one or morestripes or a grid) onto the object surface. In certain embodiments,illuminating unit 401 may be a multi-channel illumination source unit asdescribed below and shown in FIG. 5.

[0054] Illuminating unit 401 is controlled by control unit 402, whichtransmits timing and control signals to illuminating unit 401. Forexample, control unit 402 may control the spatial structure of theprojected patterns, that is control unit 402 may control whetherilluminating unit 401 illuminates objects in an area evenly or whetherit projects a pattern onto the objects. Control unit 402 may alsocontrol temporal functions, such as the length of time or frequency ofthe illumination. In addition, control unit 402 may also controlspectral modulations, such as, for example, the wavelength of thegenerated light. Control unit 402 may be controlled by signal processor420.

[0055] Detecting unit 405 may be any suitable device that forms an imageof the object or objects in the area on the sensing elements ofphotoregistrar 404. Photoregistrar 404 may be, for example, aphotodetector array, a typical CCD or CMOS sensor array comprising anarray of sensing elements (pixels), or any other device suitable fordetecting the reflected radiation. Photoregistrar 404 receives analoginformation describing an object of interest and transforms the analoginformation obtained from its sensing elements into digital code that istransmitted to signal processor 420.

[0056] Signal processor 420 may perform pre-processing of the digitalcode and store the pre-processed code into memory 425. Signal processor420 may control illumination unit 401 and detecting unit 405 via controlunits 402 and 406.

[0057] The pre-processed code, such as code for 3D images, may then beprocessed, for example, by a processor of the 3D sensor or a processorof another system (not shown). Such processing may include comparingdata representing a 3D image of an object to data of a biometricfeatures database for image recognition.

[0058] In at least one embodiment, detecting unit 405 may include one ormore parts of a multi-channel detector as described below and shown inFIG. 6. Control unit 406 transmits timing and control signals todetecting unit 405. Detecting unit 405 may be positioned to receiveprimarily light transmitted from a neighboring illumination unit andreflected from the surface of an object in the area or transmitted froman opposing illumination unit and passing through the area.

[0059] In at least one embodiment of the present invention, one or more3D sensors 400 may be mounted on an electromechanical drive, such asgyrodrive 408, that allows the 3D sensors to be positioned or moved forstabilization or targeting and is controlled by control units 402 and406 or by another processor. In certain embodiments, 3D sensors may bemoved independently of one another or moved in unison. In otherembodiments, some parts of 3D sensor 400 may be mounted onelectromechanical drive. For instance, illuminating unit 401 anddetecting unit 405 may be mounted so that they may be moved together orseparately by an electro-mechanical drive, which may be controlled bycontrol units 402 and 406 or by another processor. In other embodiments,illuminating unit 401 or detecting unit 405 may have an additional zoomlens with an electromechanical drive to zoom on different distances,which may be controlled by control units 402 and 406 or by anotherprocessor.

[0060] As mentioned above, in certain embodiments consistent with thepresent invention, illuminating unit 401 may be a multi-channelillumination unit as shown in FIG. 5. As shown in FIG. 5, an exemplaryilluminating unit comprises at least one light source 510A-51 ON, atleast one spatial light modulator (SLM) 515A-515N, and/or at least oneobjective lens 520A-520N, wherein “N” hereinafter represents a variable.

[0061] Light sources 510A-510N may generate light beams. In at least oneembodiment, one or more light source 510A-510N can generate light of adifferent spectral range, for example, ranges of the ultraviolet,visible and infra-red spectra of electromagnetic radiation. Thus, in onesuch embodiment, light from one to N spectral ranges may be projected onobject 560 from the exemplary illuminating unit.

[0062] In another embodiment, light sources 510A-510N project light ofsimilar spectral ranges and spatial light modulators (SLM) 515A-515N actas spectral filters of different spectral ranges, such that light fromone to N spectral ranges may be projected on object 560 from theexemplary illuminating unit. Alternatively, one or more spectral filtersmay be oriented within the illuminating unit for filtering light basedon spectral range prior to its projection on object 560.

[0063] The light from light sources 510A-510N is passed through SLMs515A-515N. SLMs 515A-515N may be used as code masks with, for example,patterns such as grids, or line structures and used for determining apattern of light projected onto the object 560. SLMs 515A-515N may bedistinct from each other such that the different patterns of lightprojected on object 560 are unique.

[0064] After passing through SLMs 515A-515N, the light is directed byobjective lens 520A-520N toward beam generator 530. For example, beamgenerator 530 may be a pyramid, or one or more mirrors. Beam generator530 directs the light projected by the light sources 510A-510N towardmain lens 550.

[0065] Main lens 550 is located at a proper distance from object 560 toform an optical image of the structured illumination on the surface ofobject 560.

[0066]FIG. 6 shows an exemplary multi-channel detection system foracquiring and processing 3D images. As shown in FIG. 6, a multi-channelunit 600 consistent with the present invention may comprise a main lens610, a beam splitter 615, additional lenses 620A-620N, detectors642A-642N, analog-to-digital converters (ADC) 644A-644N, signalprocessors 660A-660N, and optionally processor 670 and memory 680. Eachdotted contour, isolating detector 642N and ADC 644N into a separateunit, may represent a photoregistrar 640N. The dotted contour isolatingprocessor 670 and memory 680 into a separate unit represents anelectronic unit 690 that sums the data representing the images.

[0067] Main lens 610 is located at a proper distance from object 695 toform an optical image of the surface of object on sensor planes ofdetectors 642A-642N. Beam splitter 615 may consist of, for example, apyramid, or one or more mirrors, which is positioned behind main lens610 so that main lens 610 is located between the object 695 and beamsplitter 615. Furthermore, the position of beam splitter 615 is suchthat it forms an angle, for example, 45 angular degrees, to the opticalaxis of main lens 610. In one embodiment, beam splitter 615 is the samestructure as beam generator 530 depicted in FIG. 5.

[0068] An additional lens 620A-620N may be located in each of the Nchannels formed by beam splitter 615 and project images of thestructured illumination distorted by an object's surface onto detectors642A-642N. Detectors 642A-642N may be one or more of, for example, CCDs,CMOSs, or any other suitable sensor array detecting device. The outputsof each detector 642A-642N may be connected to the corresponding inputsof each ADC 644A-644N. ADCs 644A-644N convert the detected image into adigital code. Detectors 642A-642N together with ADCs 644A-644N formphotoregistrars 640A-640N.

[0069] The outputs of the photoregistrars 640A-640N are connected to theinputs of signal processors 660A-660N. Signal processors 660A-660Ncompute the digital input of the structured illumination images. Theoutputs of signal processors 660A-660N may be connected to the input ofelectronic unit 690. Electronic unit 690 sums the data representing thereceived images. Memory 680 together with processor 670 form electronicunit 690. Memory 680 stores the data of structured illumination imagesprocessed by photoregistrars 640A-640N for processing by processor 670.

[0070] Methods of Operating 3-D Sensors

[0071] The exemplary 3D-sensors may be operated in the followingexemplary manner, as described with reference to FIGS. 5 and 6. Forexample, as shown in FIG. 5, one or more light sources 510A-510Nilluminate SLM devices 515A-515N in different spectral ranges. The lightis focused by additional lenses 520A-520N and directed at beam generator530. Beam generator 530 reflects the light toward lens 550, whichprojects the structured light on the surface of object 560.

[0072] As shown in FIG. 6, the light is reflected by object 695 (whichmay be the same as object 560 in FIG. 5) and passes back through lens610. Lens 610 directs the light to beam splitter 615, which reflects thelight toward one or more additional lens 620A-620N. The light passesthrough the one or more lens 620 a-620 n and is registered by one ormore photoregistrars 640A-640N.

[0073] When structured illumination using differing spectral ranges isused, each of the N spectral ranges is registered by at least onecorresponding photoregistrar of identical spectral sensitivity. Thus,each image of the structured illumination distortions, formed byheterogeneities of a shape of the object surface, is registered in atleast one channel of at least one multi-channel unit of imageregistration and processing. Consequently, once the object isilluminated, N images of the structured illumination distorted by theobject surface are registered in different spectral ranges. Those Nimages represent different versions of distorted patterns formed bystructured illumination. More specifically, each photoregistrar maydetect a digital image of one version of the distorted patterns. Digitalimages differ from another because of the adjustment of the spectralrange.

[0074] As shown in FIG. 6, digital image data from each of the one ormore photoregistrars is passed to at least one signal processor660A-660N. Each signal processor 660A-660N recognizes and processes oneversion of distorted patterns, such as an aperiodic system of strips.The coding sequence for the pattern of structured illumination maydepend on the pattern projected by SLM devices 515A-515N of FIG. 5. Forinstance, in a system utilizing aperiodic strips, a “1” may be generatedwhen a line is present, and when a line is absent, a “0” may beproduced. The output of this exemplary coding sequence is shown in FIGS.7a and 7 b. Consistent with the present invention, the system may,however, utilize other patterns or types of structured light, such as agrid pattern. In addition, other coding schemes for coding the distortedpatterns may be utilized.

[0075] The resultant processed digital signal such as reconstructed 3Dtopology from signal processors 660A-660N may be accumulated inelectronic unit 690. For example, processor 670 of electronic unit 690may sum the signals received from each of signal processors 660A-660N tocreate a “overall” digital image. In addition to summarizing the binarysignals, processor 670 may determine the coordinates values (X,Y) of theobject's surface. As a result, each line (or strip) in the “overall”digital image may have a unique number in binary code. Based on thesummarized codes, processor 670 can then calculate the distance, Z, andcorresponding pairs of coordinates because distances between the stripsforming structural illumination differ on the registered picture. PortalRecognition System

[0076] The principles of the present invention may be used in a widevariety of embodiments, one of which is shown in FIG. 8. FIG. 8 shows anexemplary portal recognition system which is suitable, for example, foruse in the field of security as a means for controlling access to arestricted area. Consistent with the present invention, the portalrecognition system shown in FIG. 8 provides recognition of an objectthat is within or moving through a portal. A portal may include, forexample, a gate, doorway or area in which an object rests in or movesthrough. The portal, however, may be of any suitable size, shape orconfiguration necessary for encompassing the desired field of view andfor capturing image data.

[0077] System 800, as shown in FIG. 8, comprises 3D sensors, 810A-810F.3D sensors may be operatively connected to central processor 815 and,directly or indirectly to, memory 820 via bus 825. Additionally, thesensors in FIG. 8 may be arranged such that they surround portal 830.Image data of an object 835, such as a person's face, may be captured,for example, while the object is positioned in or moving through portal830.

[0078] At least some, if not all, of the 3D sensors are oriented so thatwhen a illumination unit 401, included in one of 3D sensor, acts as asource of illumination, a detecting unit 405 of the same or other 3Dunit is positioned around the portal so as to be able to receive theillumination reflected from the object 835 which moves through theportal 830. In one embodiment, the 3D sensors are angled to captureimages of a person's face as the person moves through the portal. Toaccount for the angle or movement of the person's face or head, the 3Dsensors may be angled such that the system can capture images of a largefield of view, such as a 180 degrees view. Additionally, at least some,if not all of the 3D sensors are at an acute angle with a neighboringcamera when measured from the center of the portal. 3D sensors that areat an acute angle away from one another may be used forstereo-photogrammetry. The 3D sensors may also be used for structuredillumination probing.

[0079]FIG. 8 shows one exemplary system that comprises 3D sensors, eachconnected to a bus 825, which may be optionally connected to a processor815 and a shared memory 820. It should be noted that other exemplaryconfigurations are possible. For example, one or more of the 3D sensorsmay be operatively connected via a bus directly to processor 815 and/orshared memory 820 or in a ring configuration.

[0080] While the embodiment depicted in FIG. 8 is shown with six 3Dsensors for simplicity, it should be understood that embodiments of thepresent invention may comprise any number of 3D sensors. In someapplications, such as particularly security systems, it may bepreferable to have a system that comprises more than six 3D sensors sothat there exist one or more redundant 3D sensors, such that the systemmay continue to function if one or more 3D sensors become inoperablethrough malfunction or are rendered inoperable by hostile forces. Inaddition, the number of 3D sensors used may also depend on the size ofthe portal containing the object.

[0081] In one embodiment, at least one of the 3D sensors may be a“smart” 3D sensor such that it includes a processor and/or memory. Forexample, on one embodiment, the 3D sensor of FIG. 4 may be used in thesystem of FIG. 8.

[0082] Consistent with the present invention, a 3D recognition system,such as the system depicted in FIG. 8, may comprise one or more 3Dsensors that acquire 3D image data representing a 3D image of an objectfor subsequent computer-aided processing of that 3D image data andidentification of the object. In one embodiment, the exemplary 3Drecognition method for processing the 3D image data and identifying theobject shown in FIG. 2 may be used.

[0083] Wide Area 3D Recognition System

[0084]FIG. 9 shows the functional layout of another exemplary embodimentconsistent with the present invention. The system shown in FIG. 9, anddescribed in further detail below, is one example of a system that maybe suitable for recognizing objects or persons that are in or passingthrough a wide area or space. Exemplary applications include securitysystems for public places such as, airports, lobbies, or other areaswhere crowds of people may be in or passing through a rather largespace. The system shown in FIG. 9 is also one example of a recognitionsystem that does not require cooperation, that is, the object or persondoes not necessarily need to voluntarily pass through a controlledspace, such as a portal, or stand in front of a recognition device.

[0085] System 900, as shown in FIG. 9, comprises 3D sensors d1, d2 . . .d6 (910, 915, 920, 925, 930, and 935, respectively). Three-D sensors910, 915, 920, 925, 930, and 935 optionally may be operatively connectedto processor 940 and, directly or indirectly to, memory 945 via bus 960.Additionally, the six 3D sensors in FIG. 9 are arranged in pairs, suchthat each 3D sensor is directly opposite another 3D sensor. The pairsare further arranged such that the pairs of 3D sensors surround an area,also referred to herein as the “controlled area.” An object of interest,such as bomb 901 or subject 902, may be identified while stationary inor moving through the controlled area.

[0086] While the embodiment depicted in FIG. 9 is shown with six 3Dsensors for simplicity, it should be understood that embodiments of thepresent invention may comprise any number of 3D sensors. In securityapplications, for example, it may be preferable to have a system thatcomprises more than six sensors so that there exist one or moreredundant 3D sensors, such that the system may continue to function ifone or more 3D sensors become inoperable through malfunction or arerendered inoperable by hostile forces.

[0087] As explained above and depicted in FIG. 4, one embodiment of a 3Dsensor includes an illuminating unit and a detecting unit. At leastsome, if not all, of the pairs of 3D sensors are oriented so that whenone of a pair of 3D sensors is acting as a source of radiation, theother is positioned across the controlled area so as to be able toreceive the radiation. Pairs of 3D sensors that are located directlyacross the controlled area from one another may be used for tomography.Additionally, at least some, if not all of the sensors, are located sothat they are at an acute angle with a neighboring 3D sensor whenmeasured from the center of the controlled area. For example, as shownin FIG. 9, 3D sensor 920 is at an acute angle from 3D sensor 915.Sensors that are at an acute angle away from one another may be used forstereo-photogrammetry.

[0088]FIG. 9 shows one exemplary system that comprises multiple 3Dsensors, each connected to a bus in the shape of a ring, which may beoptionally connected to a processor 940 and a shared memory 945. Itshould be noted that other exemplary configurations are possible. Forexample, one or more of the 3D sensors may be operatively connected viaa bus directly to processor 940 and/or shared memory 945. In anotherembodiment, one or more of 3D sensors may comprise a processor and/ormemory and the 3D sensors may be operatively configured such that theprocessors and/or memories of the individual 3D sensors are sharedand/or work in parallel. In certain embodiments, any processors and/ormemories of the 3D sensors may be configured as a “cluster,” that is, acoherent, parallel PC computing system such as a Beowulf cluster. ABeowulf cluster generally consists of a collection of workstationsconnected through one or more networks. These systems generally use opensource system software to provide the features expected of a parallelcomputer, such as message passing, process management, and globalstorage.

[0089] An exemplary 3D recognition method for processing the 3D imagedata of an exemplary wide area and identifying the object is shown inFIG. 10. As depicted in FIG. 10, 3D sensors may collect initial imagedata representing the HENDERSON objects or persons in the controlledarea. In at least one embodiment consistent with the present invention,the initial image data is collected using tomography, and therefore isreferred to herein as tomographic data (Stage 1005). Using the collectedimage data, the system processes the image data to determine if one ormore zones of interest are detected within the controlled area (Stage1010).

[0090] If no zones of interest are detected, the initial image datarepresenting the controlled area may again be collected (Stage 1005). Inat least one embodiment, the additional initial image data is collectedusing tomography. Upon detection of a zone of interest, the system mayidentify a pair of 3D sensors to perform stereo-photogrammetry of thezone of interest (Stage 1020). The identified stereo pair of 3D sensorsmay be used to collect image data, otherwise called stereo-photographicdata, from the zone of interest. Based on the collectedstereo-photographic data, the initial image data of the zone of interestmay be refined one or more times to remove noise or other data which canbe identified as unrelated to the objects of interest (Stage 1025). Thesystem may then determine if one or more objects of interest aredetected based on the refined 3D image data (Stage 1030).

[0091] If no objects of interest are detected, the initial image datarepresenting the controlled area may again be collected (Stage 1005). Inat least one embodiment, the additional initial image data is collectedusing tomography. Upon detection of an object of interest, the systemmay determine the distance from the object of interest to one of thesystems' 3D sensors (Stage 1035). That distance may be calculated fromthe collected data using, for example, range finding and/ortriangulation techniques as known to those skilled in the art and whichare also described in more detail herein. The determined distance fromthe object to the 3D sensor may be used to focus the 3D sensor's fieldof view on the object of interest (Stage 1040). The system may then usestructured illumination to acquire 3D image data representing the objectof interest (Stage 1045). From this 3D image data, some of the featuresof the object of interest are extracted (Stage 1050). For example, afeature vector representing a subset of the 3D image data may begenerated.

[0092] Based on the extracted features, the system may determine if theobject of interest can be identified (Stage 1055). For example, thefeature vector may be compared to data in a database.

[0093] If the object of interest is not identified, initial image datarepresenting the controlled area may be collected again (Stage 1005). Inat least one embodiment, the additional initial image data is collectedusing tomography. If, however, the object of interest is identified,then the system's identification of the object is complete.

[0094] In one embodiment, detection of a zone of interest or object ofinterest within the controlled area includes comparison of thedetermined 3D image data with, for example, a set of rules governing thecontrolled area. In another embodiment, the determined 3D image data iscompared with a database of biometric data representing 3Dobjects-of-interest. In certain embodiments, the database may containonly data representing “alarming” data or objects such as, in thesecurity system example, images of known or suspected terrorists orweapons or other dangerous objects. In certain other embodiments, thedatabase may contain only data representing “safe” data or, for example,individuals or objects that should be allowed access to a controlledarea. In yet another embodiment, the database may contain datarepresenting both “alarming” and “safe” data. The database containingsuch data may reside in the memory of one or more of the 3D sensorsd1-dn (See FIG. 9), within a memory 945 attached to the cluster via bus960, or within another such memory.

[0095] Consistent with the present invention, the aforementioned methodand system use 3D image data to provide both macrolevel recognition(e.g., detection of one or more zones or objects of interest within acontrolled area) and microlevel recognition (e.g., identification of anobject of interest within the controlled area). Since these operationsmay be repeated cyclically, the system can monitor the controlled areain automatic, semi-automatic, and manual modes of observation.Furthermore, because the system can continuously capture 3D images at afast rate, the probability of error is low and decreases as the numberof frames taken increases. A low false-acceptance rate as well as a lowfalse-rejection rate is desirable in security systems.

[0096] It should be noted that certain embodiments of the methods andsystems described herein remain operative even if one or more cameras,projectors, or 3D sensors are disabled. Consistent with the presentinvention, a system may utilize a smaller or a larger number of cameras,projectors, or 3D sensors. In fact, in a system using a large number ofthese devices, many of the devices may be disabled before the systemexperiences a degradation in performance. Such system performance isalso based upon the system configuration. For instance, where the 3Dsensors process images and perform recognition in parallel, thedisabling of one or more 3D sensors does not degrade system performancebecause working 3D sensors perform the necessary system processing.

[0097] The aforementioned systems and methods of the present inventionmay be implemented utilizing various techniques, such as structuredillumination, computed tomography, stereo-photogrammetry, range findingand triangulation.

[0098] Computed Tomography

[0099] The present invention may use principles of computed tomography(“CT”) in the collection of 3D image data. In computed tomography, aninfrared or other light source projects a fan-shaped beam which iscollimated to lie within an X-Y plane of a Cartesian coordinate systemand generally referred to as the “imaging plane”. The light beam passesthrough the object being imaged. The beam, after being attenuated by theobject, impinges upon one or more radiation detectors. Multipledetectors may be arranged in the form of a detector array. The intensityof the attenuated beam radiation received at the detector is dependentupon the attenuation of the beam by the object. Each detector element ofan array produces a separate electrical signal that is a measurement ofthe beam attenuation at the detector location. The attenuationmeasurements, or projection data, from all the detectors are acquiredseparately to produce a transmission profile.

[0100] In conventional CT systems, the light source and the detectorarray are rotated within the imaging plane and around an object to beimaged so that the angle at which the beam intersects the objectconstantly changes. A group of beam attenuation measurements, i.e.,projection data, from the detector array at one angle is referred to asa “view”. A “scan” of the object comprises a set of views made atdifferent angles during one revolution of the light source and detector.The projection data is then processed to construct an image thatcorresponds to a two-dimensional slice taken through the object.

[0101] Consistent with the present invention computed tomography is usedto collect the initial image data, otherwise called tomographic data,representing the controlled area. In one embodiment, the controlled areais subjected to illumination from at least two light sources. The lightsources may or may not simultaneously illuminate the controlled area.Tomographic data is then gathered by at least two of the 3D sensors.

[0102] Computed Tomography and Structured Illumination

[0103] Consistent with the present invention, computed tomography may beperformed with or without use of the structured illumination. In oneexemplary embodiment, the controlled area is subjected to computedtomography without structured illumination. First, pairs of opposite 3Dsensors collect shady projections of the controlled area. In each of twoor more pairs of 3D sensors, the light source in one of the 3D sensorsin a pair generates a source of radiation, such as an infrared beam, andprojects the source of radiation through the controlled area. In eachpair, a detector in the opposing 3D sensor pair perceives the probingradiation from the opposite 3D sensor. A processor may control pairs ofopposite 3D sensors so that when one of the sensors serves as a sourceof radiation, the other works as a detector, and vice versa.

[0104] Each detector element produces an electrical signal thatrepresents the intensity of the received beam and records theattenuation of the beam as it passes through the controlled area. Theanalog data received by the detectors is converted to digital signalscalled projections for subsequent processing. The projections are storedin memory. If each pair of 3D sensors operates as both a projector and adetector, two opposite groups of projection data (a “pair”) may becollected and stored.

[0105] Without structured illumination, the resulting image from onepair of sensors is a shadowy projection of the object. The projectionsmay be used to generate an initial image representing a group of objectsor a single object in the controlled area. A 3D representation of theobject or objects in the controlled area may be reconstructed, forexample, by using the 3D Radon transform. Radon pioneered the idea thatif one collects enough 2D projections from various angles, one cancompletely reconstruct a 3D image. Methods and algorithms forreconstructing a representation of a 3D object using the 3D Radontransform, such as those employed in medical computed tomography (CT)systems, are well known to those skilled in the art.

[0106] In one embodiment, an image of the one or more objects in thecontrolled area is reconstructed by summing the “reverse projections”.Illuminating units illuminate the surface of the objects using opticalradiation of different foreshortenings. Image data, or projections, ofthe illuminated objects at each foreshortening may be collected frommultiple detectors, each located at known angles. (See FIG. 11A). Theprojections obtained from each detector are transferred or “reverseprojected” to produce a “reverse projection”. The reverse projectionsare rotated at an angle corresponding to the illumination foreshorteningand then summarized to create a summary image. (See FIG. 11B). Bymultiplying the number of projections, heterogeneities in the imagesinterfere and consequently cancel each other out. In general, the methodof “reverse projecting” and the associated algorithms are well known tothose skilled in the art.

[0107] In another exemplary embodiment, the controlled area is subjectedto computed tomography with structured illumination. Fourier analysismay be employed to determine the topology of the surface of the objectwhen tomography is performed with structured illumination. Fourieranalysis of projections may be used to reconstruct the tomogram usingimage data gathered with structured illumination.

[0108] Fourier analysis and the associated algorithms are well known tothose in the art.

[0109] Stereo-Photogrammetry

[0110] Additional image data of a zone of interest may be obtained, forexample, by combining the tomographic data with data from 3D sensorsusing known stereo photographic principles. In one embodiment, thetomographic data and data from the stereo pair, otherwise calledstereophotographic data, are collected at the same time. In anotherembodiment, the stereophotographic data is collected after thetomographic data is collected, for example, by performing a second datacollection after a zone of interest is detected. Based onstereophotographic data and tomographic data, the image data is refinedand a more refined 3D image of a zone of interest within the controlledarea is determined.

[0111] Stereo-photogrammetry is a technique used to obtain 3Dmeasurements of an object through the process of recording and measuringa pair of images taken by a pair of detectors. In particular,stereo-photogrammetry is used to determine the third coordinate of thespatial location of a point namely a Z-coordinate called distance. Allthree coordinates of a point on an object, X, Y, and Z, cannot bedetermined from only one shot or photograph of an object because itprovides only two measured values, coordinates X and Y. Instead, it isnecessary to use a second shot of the object wherein the first andsecond shots are taken from different points in space.

[0112] The simplest case of image acquisition (from geometrical standpoint) arises when the basis and shots are horizontal. In this scenario,the optical axis of the detector and basis are in the same plane andperpendicular to each other. In certain embodiments, at leastneighboring, or proximate, 3D sensors are used to collect image data.

[0113] All three coordinates X, Y, and Z of a point on the object may bedetermined using measured (known) coordinates of the image point on thetwo or more images as well as intersection formulas. Presumably, thepositions and angles of the detectors are known and the two images maybe described in two dimensions, X and Y. After determining thecorrelation between the two images, the differences between the twoimages are determined and used to estimate the distance of the imagefrom the detectors (or the third dimension, Z). The mathematicalprinciples of stereo-photogrammetry are well known to those skilled inthe art.

[0114] In lieu of determining distance, however, excess, or height of apoint above a constant is determined. Formulas of excess can beconsidered as special case of the intersection formulas because excessis an increment of distance. Formulas of excess are differentialformulas of distance and are well known to those skilled in the art.

[0115] Range Finding and Triangulation

[0116] The principles of range finding and triangulation may be used todetermine the distance of an object from two 3-D sensors. Atriangulation system projects beams of light on an object and thendetermines 3D spatial locations for points where the light reflects fromthe object.

[0117] As depicted in FIG. 12, axis of the sensors 3DDi and 3DDi+1 forma measurement triangle. Since the distance between the sensors (BS),parallax angle ε, and the angles α and γ between axis of the 3-D sensorsare measured and known, the distance to object (L) can be determined bytriangulation.

[0118] Alternative embodiments will become apparent to those skilled inthe art to which the present invention pertains without departing fromits spirit and scope. For instance, the present invention also relatesto computer readable media that include program instruction or programcode for performing various computer-implemented operations based on themethods and processes of the invention. The program instructions may bethose specially designed and constructed for the purposes of theinvention, or they may be of the kind well-known and available to thosehaving skill in the computer software arts. Examples of programinstructions include, for example, machine code produced by a compiler,and files containing a high level code that can be executed by thecomputer using, for example, an interpreter or equivalent executionengine to facilitate execution of high level code.

[0119] Accordingly, the scope of the present invention is defined by theappended claims rather the foregoing description.

What is claimed is:
 1. A method of identifying an object of interest ina controlled area surrounded by at least two pairs of 3D sensors, themethod comprising: collecting first 3D image data representing thecontrolled area using at least two pairs of 3D sensors surrounding thecontrolled area; detecting a zone of interest within the controlled areabased on the first 3D image data; collecting second image datarepresenting the detected zone of interest utilizing at least one pairof 3D sensors; and identifying an object of interest in the controlledarea based on the second 3D image data.
 2. The method of claim 1,wherein identifying an object of interest in the controlled area basedon the second 3D image data further includes: detecting an object ofinterest within the detected zone of interest based on the second 3Dimage data; collecting third 3D image data representing the detectedobject of interest utilizing at least one 3D sensor; and comparing thethird 3D image data to data representing known objects.
 3. A method ofidentifying an object of interest in a controlled area, the methodcomprising: collecting first 3D image data representing the controlledarea; detecting a zone of interest within the controlled area based onthe first 3D image data; collecting second image data representing thedetected zone of interest; detecting an object of interest within thedetected zone of interest based on the second 3D image data; collectingthird 3D image data representing the detected object of interest;comparing the third 3D image data to data representing known objects;and identifying an object of interest in the controlled area based onthe third 3D image data.
 4. The method of claim 3, wherein collectingfirst 3D image data representing the controlled area includes utilizingtomography.
 5. The method of claim 4, wherein collecting first 3D imagedata representing the controlled area further includes utilizing atleast two pairs of 3D sensors to collect image data.
 6. The method ofclaim 3, wherein collecting second 3D image data of the zone of interestincludes utilizing stereo-photogrammetry.
 7. The method of claim 6,wherein collecting second 3 d image data of the zone of interest furtherincludes utilizing at least one pair of 3D sensors to collect imagedata.
 8. The method of claim 3, wherein collecting third 3d image dataof the object of interest includes utilizing structured illumination. 9.The method of claim 8, wherein collecting third 3D Image data of theobject of interest further includes determining the coordinates of theobject of interest based on the second 3D image data.
 10. The method ofclaim 9, wherein determining the coordinates of the object of interestbased on the second 3D image data includes utilizing triangulation. 11.The method of claim 9, wherein determining the coordinates of the objectof interest based on the second 3D image data includes determining adistance between the object of interest and at least one 3D sensor. 12.The method of claim 3, wherein identifying the object of interest basedon the third 3D image data includes: generating a feature vector basedon the third 3D image data, the feature vector representing features ofthe detected object of interest; and comparing the feature vector todata representing features of known objects.
 13. The method of claim 3,wherein identifying the object of interest based on the third 3D imagedata includes: generating a feature vector based on the second 3D imagedata, the feature vector representing features of the object; andconstructing a filter vector based on the feature vector; determining asubset of data representing known objects based on the filter vector;and comparing the second 3D image data to the subset of datarepresenting features of known objects.
 14. The method of claim 3,wherein collecting first 3D image data representing the controlled areaand collecting second 3D image data of the zone of interest occur eithersimultaneously or in succession.
 15. The method of claim 3, furthercomprising checking the quality of some or all of the first, second, orthird 3D image data.
 16. The method of claim 15, wherein checking thequality of some or all of the first, second, or third 3D image datacomprises utilizing a rejection algorithm.
 17. The method of claim 16,wherein the rejection algorithm includes at least one of the steps of:determining if the first, second, or third 3D image data is good; anddetermining if the first, second, or third 3D image data comprisesenough data for recognition.
 18. A method of identifying an object in acontrolled area based on 3D image data, the method comprising: obtainingfirst 3D image data representing the controlled area; detecting a zoneof interest within the controlled area based on the first 3D image data;obtaining second image data representing the detected zone of interest;detecting an object of interest within the detected zone of interestbased on the second 3D image data; obtaining third 3D image datarepresenting the detected object of interest; comparing the third 3Dimage data to data representing known objects; and identifying an objectof interest in the controlled area based on the third 3D image data. 19.A system for identifying an object in a controlled area surrounded by atleast two pairs of 3D sensors, the system comprising: at least two pairsof 3D sensors, each pair comprising a first and a second 3D sensor, eachfirst 3D sensor in a pair of 3D sensors is situated directly across thecontrolled area from its corresponding second 3D sensor, each 3D sensorcomprising: an illumination unit capable of illuminating the controlledarea and a registration unit comprising: a detecting unit capable ofdetecting light reflected by at least one of the controlled area and theobject, and an analog-to-digital converter for converting the detectedlight into digital data; and a processing unit capable of generating 3Dimage data based on the converted data.
 20. The system of claim 19,wherein the processing unit is capable of identifying the object basedon the generated 3D image data.
 21. The system of claim 19, wherein atleast two 3D sensors are operatively connected for operating inparallel.
 22. The system of claim 19, wherein one or more 3D sensorsfurther comprise a memory unit capable of storing 3D image data.
 23. Asystem for identifying an object of interest in a controlled area, thesystem comprising: an illumination unit capable of illuminating thecontrolled area and the object; and a registration unit comprising: adetecting unit capable of detecting light reflected by at least one ofthe controlled area and the object, and an analog-to-digital converterfor converting the detected light into digital data; and a processingunit capable of generating 3D image data based on the converted data.24. The system of claim 23, wherein the processing unit is capable ofidentifying the object based on the generated 3D image data.
 25. Asystem for identifying an object of interest in a controlled area, thesystem comprising: a processing unit capable of obtaining first 3D imagedata representing the controlled area; detecting a zone of interestwithin the controlled area based on the first 3D image data; obtainingsecond image data representing the detected zone of interest; detectingan object of interest within the detected zone of interest based on thesecond 3D image data; obtaining third 3D image data representing thedetected object of interest; comparing the third 3D image data to datarepresenting known objects; and identifying an object of interest in thecontrolled area based on the third 3D image data; and a memory unitcapable of storing 3D image data.
 26. A computer readable mediumcontaining instructions for controlling a computer system to perform amethod of identifying an object of interest in a controlled areasurrounded by at least two pairs of 3D sensors, the method comprising:collecting first 3D image data representing the controlled area using atleast two pairs of 3D sensors surrounding the controlled area; detectinga zone of interest within the controlled area based on the first 3Dimage data; collecting second image data representing the detected zoneof interest utilizing at least one pair of 3D sensors; and identifyingan object of interest in the controlled area based on the second 3Dimage data.
 27. A computer readable medium containing instructions forcontrolling a computer system to perform a method of identifying anobject of interest in a controlled area, the method comprising:collecting first 3D image data representing the controlled area;detecting a zone of interest within the controlled area based on thefirst 3D image data; collecting second image data representing thedetected zone of interest; detecting an object of interest within thedetected zone of interest based on the second 3D image data; collectingthird 3D image data representing the detected object of interest;comparing the third 3D image data to data representing known objects;and identifying an object of interest in the controlled area based onthe third 3D image data.
 28. A computer readable medium containinginstructions for controlling a computer system to perform a method ofidentifying an object in a controlled area based on 3D image data, themethod comprising: obtaining first 3D image data representing thecontrolled area; detecting a zone of interest within the controlled areabased on the first 3D image data; obtaining second image datarepresenting the detected zone of interest; detecting an object ofinterest within the detected zone of interest based on the second 3Dimage data; obtaining third 3D image data representing the detectedobject of interest; comparing the third 3D image data to datarepresenting known objects; and identifying an object of interest in thecontrolled area based on the third 3D image data.